Overview

Dataset statistics

Number of variables29
Number of observations9999
Missing cells92654
Missing cells (%)32.0%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory2.2 MiB
Average record size in memory232.0 B

Variable types

Categorical14
Numeric15

Alerts

event has constant value "Vehicle Connected"Constant
Dataset has 3 (< 0.1%) duplicate rowsDuplicates
vehicle_id has a high cardinality: 3421 distinct valuesHigh cardinality
user_id has a high cardinality: 3454 distinct valuesHigh cardinality
occurred_at_time has a high cardinality: 9718 distinct valuesHigh cardinality
app_version has a high cardinality: 107 distinct valuesHigh cardinality
battery_cells has a high cardinality: 782 distinct valuesHigh cardinality
battery_serial_number has a high cardinality: 4408 distinct valuesHigh cardinality
hmi_serial_number has a high cardinality: 3589 distinct valuesHigh cardinality
battery_error_state is highly overall correlated with versions_mc_firmwareHigh correlation
battery_state_battery_soc is highly overall correlated with battery_state_pack_voltage and 1 other fieldsHigh correlation
battery_state_cell_temp_1 is highly overall correlated with battery_state_cell_temp_2 and 6 other fieldsHigh correlation
battery_state_cell_temp_2 is highly overall correlated with battery_state_cell_temp_1 and 6 other fieldsHigh correlation
battery_state_chg_temp is highly overall correlated with battery_state_cell_temp_1 and 6 other fieldsHigh correlation
battery_state_dsg_temp is highly overall correlated with battery_state_cell_temp_1 and 6 other fieldsHigh correlation
battery_state_full_capacity is highly overall correlated with battery_state_cell_temp_1 and 8 other fieldsHigh correlation
battery_state_pack_voltage is highly overall correlated with battery_state_battery_soc and 2 other fieldsHigh correlation
battery_state_pre_start_temp is highly overall correlated with battery_state_cell_temp_1 and 6 other fieldsHigh correlation
battery_state_remaining_capacity is highly overall correlated with battery_state_cell_temp_1 and 6 other fieldsHigh correlation
remaining_mileage is highly overall correlated with battery_state_battery_soc and 1 other fieldsHigh correlation
versions_mc_firmware is highly overall correlated with battery_error_stateHigh correlation
os_name is highly overall correlated with battery_state_cell_temp_1 and 7 other fieldsHigh correlation
versions_battery_firmware is highly overall correlated with battery_state_full_capacity and 1 other fieldsHigh correlation
versions_hmi_firmware is highly overall correlated with battery_state_full_capacity and 2 other fieldsHigh correlation
versions_hmi_hardware is highly overall correlated with versions_hmi_firmwareHigh correlation
versions_mc_firmware is highly imbalanced (96.5%)Imbalance
os_name is highly imbalanced (62.4%)Imbalance
versions_battery_firmware is highly imbalanced (50.1%)Imbalance
total_mileage has 1684 (16.8%) missing valuesMissing
versions_mc_firmware has 2026 (20.3%) missing valuesMissing
assist_level has 1798 (18.0%) missing valuesMissing
battery_cells has 8573 (85.7%) missing valuesMissing
battery_serial_number has 1925 (19.3%) missing valuesMissing
battery_state_battery_soc has 1907 (19.1%) missing valuesMissing
battery_state_battery_soh has 1907 (19.1%) missing valuesMissing
battery_state_cell_temp_1 has 8595 (86.0%) missing valuesMissing
battery_state_cell_temp_2 has 8595 (86.0%) missing valuesMissing
battery_state_chg_temp has 8595 (86.0%) missing valuesMissing
battery_state_dsg_temp has 8595 (86.0%) missing valuesMissing
battery_state_full_capacity has 8595 (86.0%) missing valuesMissing
battery_state_pack_voltage has 1907 (19.1%) missing valuesMissing
battery_state_pre_start_temp has 8595 (86.0%) missing valuesMissing
battery_state_real_time_current has 1907 (19.1%) missing valuesMissing
battery_state_remaining_capacity has 8595 (86.0%) missing valuesMissing
hmi_serial_number has 962 (9.6%) missing valuesMissing
remaining_mileage has 1739 (17.4%) missing valuesMissing
versions_battery_firmware has 2026 (20.3%) missing valuesMissing
versions_hmi_firmware has 2026 (20.3%) missing valuesMissing
versions_hmi_hardware has 2026 (20.3%) missing valuesMissing
total_mileage is highly skewed (γ1 = 24.03341074)Skewed
assist_level is highly skewed (γ1 = 23.94794415)Skewed
remaining_mileage is highly skewed (γ1 = 28.48081515)Skewed
occurred_at_time is uniformly distributedUniform
assist_level has 475 (4.8%) zerosZeros
battery_error_state has 9872 (98.7%) zerosZeros
battery_state_cell_temp_1 has 591 (5.9%) zerosZeros
battery_state_cell_temp_2 has 592 (5.9%) zerosZeros
battery_state_chg_temp has 594 (5.9%) zerosZeros
battery_state_dsg_temp has 594 (5.9%) zerosZeros
battery_state_full_capacity has 591 (5.9%) zerosZeros
battery_state_pre_start_temp has 594 (5.9%) zerosZeros
battery_state_remaining_capacity has 591 (5.9%) zerosZeros

Reproduction

Analysis started2023-10-05 19:39:06.590692
Analysis finished2023-10-05 19:40:25.158049
Duration1 minute and 18.57 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

vehicle_id
Categorical

Distinct3421
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
5b3a3fb6-35ff-4d10-957a-68e50b9e9117
 
84
fd878446-1c44-4a9f-b88f-10ce83cade59
 
47
382d4727-6119-4391-adc0-ac5500c489b6
 
47
ca4c5d0d-0d08-4189-b8ee-e8cd4d337422
 
39
a394bc6d-1c28-42d6-b473-88379f34d6aa
 
36
Other values (3416)
9746 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters359964
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1407 ?
Unique (%)14.1%

Sample

1st rowbc3412f7-37a8-4676-8043-53f4a1bb316a
2nd row95e7ee01-3987-481a-9000-3b331c52192f
3rd rowc63d60f2-5e2a-483c-bb11-935c45ebf2f0
4th row79df825d-8115-4a84-9aa8-87ae307ae561
5th row0d30c889-a102-4bdc-b8a3-c1e0d6c536d5

Common Values

ValueCountFrequency (%)
5b3a3fb6-35ff-4d10-957a-68e50b9e9117 84
 
0.8%
fd878446-1c44-4a9f-b88f-10ce83cade59 47
 
0.5%
382d4727-6119-4391-adc0-ac5500c489b6 47
 
0.5%
ca4c5d0d-0d08-4189-b8ee-e8cd4d337422 39
 
0.4%
a394bc6d-1c28-42d6-b473-88379f34d6aa 36
 
0.4%
8a8278e1-021c-45fc-a485-66e41291b9e7 35
 
0.4%
6eb63e1f-be68-42e6-9959-f962667b9d64 35
 
0.4%
4cf2b02f-5631-4441-a93d-a1f2d59c83bf 34
 
0.3%
9d5c8861-faa8-4c13-960b-88d1cd147917 29
 
0.3%
df816c87-7f79-4834-9dc5-efc1391c886b 29
 
0.3%
Other values (3411) 9584
95.8%

Length

2023-10-05T19:40:25.331179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5b3a3fb6-35ff-4d10-957a-68e50b9e9117 84
 
0.8%
fd878446-1c44-4a9f-b88f-10ce83cade59 47
 
0.5%
382d4727-6119-4391-adc0-ac5500c489b6 47
 
0.5%
ca4c5d0d-0d08-4189-b8ee-e8cd4d337422 39
 
0.4%
a394bc6d-1c28-42d6-b473-88379f34d6aa 36
 
0.4%
8a8278e1-021c-45fc-a485-66e41291b9e7 35
 
0.4%
6eb63e1f-be68-42e6-9959-f962667b9d64 35
 
0.4%
4cf2b02f-5631-4441-a93d-a1f2d59c83bf 34
 
0.3%
24205f9c-7987-4984-9755-bd95f3305655 29
 
0.3%
df816c87-7f79-4834-9dc5-efc1391c886b 29
 
0.3%
Other values (3411) 9584
95.8%

Most occurring characters

ValueCountFrequency (%)
- 39996
 
11.1%
4 28133
 
7.8%
8 21712
 
6.0%
9 21415
 
5.9%
b 21375
 
5.9%
a 21128
 
5.9%
1 19365
 
5.4%
5 19277
 
5.4%
7 19103
 
5.3%
3 18812
 
5.2%
Other values (7) 129648
36.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202618
56.3%
Lowercase Letter 117350
32.6%
Dash Punctuation 39996
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 28133
13.9%
8 21712
10.7%
9 21415
10.6%
1 19365
9.6%
5 19277
9.5%
7 19103
9.4%
3 18812
9.3%
6 18547
9.2%
0 18280
9.0%
2 17974
8.9%
Lowercase Letter
ValueCountFrequency (%)
b 21375
18.2%
a 21128
18.0%
c 18801
16.0%
d 18792
16.0%
f 18709
15.9%
e 18545
15.8%
Dash Punctuation
ValueCountFrequency (%)
- 39996
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 242614
67.4%
Latin 117350
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 39996
16.5%
4 28133
11.6%
8 21712
8.9%
9 21415
8.8%
1 19365
8.0%
5 19277
7.9%
7 19103
7.9%
3 18812
7.8%
6 18547
7.6%
0 18280
7.5%
Latin
ValueCountFrequency (%)
b 21375
18.2%
a 21128
18.0%
c 18801
16.0%
d 18792
16.0%
f 18709
15.9%
e 18545
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 359964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 39996
 
11.1%
4 28133
 
7.8%
8 21712
 
6.0%
9 21415
 
5.9%
b 21375
 
5.9%
a 21128
 
5.9%
1 19365
 
5.4%
5 19277
 
5.4%
7 19103
 
5.3%
3 18812
 
5.2%
Other values (7) 129648
36.0%

user_id
Categorical

Distinct3454
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2d213fa9-1951-4265-8725-5c0992b155c2
 
93
d453aad8-6c95-425a-8a63-f9fe012554f8
 
47
b16f77bf-6a58-49b0-8b8f-da7c7058bfbf
 
47
e9e81cc8-0fef-4a75-a851-7ca7ddfd117b
 
41
98f8b924-0f17-4285-898e-9b95b6de6162
 
40
Other values (3449)
9731 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters359964
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1504 ?
Unique (%)15.0%

Sample

1st rowca73adbe-5930-45ba-af74-de5865d8b093
2nd row6560e385-b90d-42be-b977-5cdb3a5d5a60
3rd row118047dc-bf44-4dc0-9862-0cb3ac5db65d
4th rowf0c0ac6d-32a1-45ed-9de1-466455fa637a
5th row2b9f8b3f-b3f3-409b-ade0-dd9ae8b54ba7

Common Values

ValueCountFrequency (%)
2d213fa9-1951-4265-8725-5c0992b155c2 93
 
0.9%
d453aad8-6c95-425a-8a63-f9fe012554f8 47
 
0.5%
b16f77bf-6a58-49b0-8b8f-da7c7058bfbf 47
 
0.5%
e9e81cc8-0fef-4a75-a851-7ca7ddfd117b 41
 
0.4%
98f8b924-0f17-4285-898e-9b95b6de6162 40
 
0.4%
1b50bc7e-15c5-4ae7-822e-9b29988adf7b 38
 
0.4%
7d7c1e29-1a35-447c-b672-5899a55bc6f1 38
 
0.4%
44ebfb1a-9cfe-4791-8fc3-f8e5a0ff9b74 38
 
0.4%
2aef8afc-6d81-4c4b-b103-9bd4db52fe61 37
 
0.4%
86ce07da-114d-48e7-8802-066b2ab4c087 36
 
0.4%
Other values (3444) 9544
95.4%

Length

2023-10-05T19:40:25.578543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2d213fa9-1951-4265-8725-5c0992b155c2 93
 
0.9%
b16f77bf-6a58-49b0-8b8f-da7c7058bfbf 47
 
0.5%
d453aad8-6c95-425a-8a63-f9fe012554f8 47
 
0.5%
e9e81cc8-0fef-4a75-a851-7ca7ddfd117b 41
 
0.4%
98f8b924-0f17-4285-898e-9b95b6de6162 40
 
0.4%
1b50bc7e-15c5-4ae7-822e-9b29988adf7b 38
 
0.4%
7d7c1e29-1a35-447c-b672-5899a55bc6f1 38
 
0.4%
44ebfb1a-9cfe-4791-8fc3-f8e5a0ff9b74 38
 
0.4%
2aef8afc-6d81-4c4b-b103-9bd4db52fe61 37
 
0.4%
86ce07da-114d-48e7-8802-066b2ab4c087 36
 
0.4%
Other values (3444) 9544
95.4%

Most occurring characters

ValueCountFrequency (%)
- 39996
 
11.1%
4 28567
 
7.9%
9 22159
 
6.2%
8 21756
 
6.0%
b 21147
 
5.9%
a 20435
 
5.7%
1 19689
 
5.5%
6 19183
 
5.3%
2 19098
 
5.3%
d 19059
 
5.3%
Other values (7) 128875
35.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 204147
56.7%
Lowercase Letter 115821
32.2%
Dash Punctuation 39996
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 28567
14.0%
9 22159
10.9%
8 21756
10.7%
1 19689
9.6%
6 19183
9.4%
2 19098
9.4%
5 19014
9.3%
7 18398
9.0%
3 18171
8.9%
0 18112
8.9%
Lowercase Letter
ValueCountFrequency (%)
b 21147
18.3%
a 20435
17.6%
d 19059
16.5%
f 18489
16.0%
e 18355
15.8%
c 18336
15.8%
Dash Punctuation
ValueCountFrequency (%)
- 39996
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 244143
67.8%
Latin 115821
32.2%

Most frequent character per script

Common
ValueCountFrequency (%)
- 39996
16.4%
4 28567
11.7%
9 22159
9.1%
8 21756
8.9%
1 19689
8.1%
6 19183
7.9%
2 19098
7.8%
5 19014
7.8%
7 18398
7.5%
3 18171
7.4%
Latin
ValueCountFrequency (%)
b 21147
18.3%
a 20435
17.6%
d 19059
16.5%
f 18489
16.0%
e 18355
15.8%
c 18336
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 359964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 39996
 
11.1%
4 28567
 
7.9%
9 22159
 
6.2%
8 21756
 
6.0%
b 21147
 
5.9%
a 20435
 
5.7%
1 19689
 
5.5%
6 19183
 
5.3%
2 19098
 
5.3%
d 19059
 
5.3%
Other values (7) 128875
35.8%

total_mileage
Real number (ℝ)

MISSING  SKEWED 

Distinct6330
Distinct (%)76.1%
Missing1684
Missing (%)16.8%
Infinite0
Infinite (%)0.0%
Mean6647965.3
Minimum0
Maximum4.2357077 × 109
Zeros83
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:25.863081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile248
Q12188.5
median5647
Q310993
95-th percentile18622.2
Maximum4.2357077 × 109
Range4.2357077 × 109
Interquartile range (IQR)8804.5

Descriptive statistics

Standard deviation1.4550904 × 108
Coefficient of variation (CV)21.887755
Kurtosis602.62127
Mean6647965.3
Median Absolute Deviation (MAD)3995
Skewness24.033411
Sum5.5277832 × 1010
Variance2.117288 × 1016
MonotonicityNot monotonic
2023-10-05T19:40:26.166878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83
 
0.8%
1 8
 
0.1%
2 8
 
0.1%
2112 8
 
0.1%
663 7
 
0.1%
63 7
 
0.1%
6 6
 
0.1%
937 6
 
0.1%
11 6
 
0.1%
2593 5
 
0.1%
Other values (6320) 8171
81.7%
(Missing) 1684
 
16.8%
ValueCountFrequency (%)
0 83
0.8%
1 8
 
0.1%
2 8
 
0.1%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 6
 
0.1%
9 1
 
< 0.1%
10 4
 
< 0.1%
11 6
 
0.1%
ValueCountFrequency (%)
4235707659 1
< 0.1%
4145238046 1
< 0.1%
4011373570 1
< 0.1%
3967496428 1
< 0.1%
3818981767 1
< 0.1%
3634652942 2
< 0.1%
3461804109 1
< 0.1%
3428659541 1
< 0.1%
3379368544 1
< 0.1%
3125455142 1
< 0.1%

versions_mc_firmware
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct7
Distinct (%)0.1%
Missing2026
Missing (%)20.3%
Memory size78.2 KiB
DA210815.10
7892 
000000.0
 
54
DA210727.9
 
16
DA210524.7
 
5
DA٢١٠٨١٥.10
 
3
Other values (2)
 
3

Length

Max length11
Median length11
Mean length10.976922
Min length8

Characters and Unicode

Total characters87519
Distinct characters16
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDA210815.10
2nd rowDA210815.10
3rd rowDA210815.10
4th rowDA210815.10
5th rowDA210815.10

Common Values

ValueCountFrequency (%)
DA210815.10 7892
78.9%
000000.0 54
 
0.5%
DA210727.9 16
 
0.2%
DA210524.7 5
 
0.1%
DA٢١٠٨١٥.10 3
 
< 0.1%
DA220125.11 2
 
< 0.1%
DA081510.0 1
 
< 0.1%
(Missing) 2026
 
20.3%

Length

2023-10-05T19:40:26.437972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T19:40:26.764310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
da210815.10 7892
99.0%
000000.0 54
 
0.7%
da210727.9 16
 
0.2%
da210524.7 5
 
0.1%
da٢١٠٨١٥.10 3
 
< 0.1%
da220125.11 2
 
< 0.1%
da081510.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 23708
27.1%
0 16191
18.5%
. 7973
 
9.1%
2 7940
 
9.1%
D 7919
 
9.0%
A 7919
 
9.0%
5 7900
 
9.0%
8 7893
 
9.0%
7 37
 
< 0.1%
9 16
 
< 0.1%
Other values (6) 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63708
72.8%
Uppercase Letter 15838
 
18.1%
Other Punctuation 7973
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23708
37.2%
0 16191
25.4%
2 7940
 
12.5%
5 7900
 
12.4%
8 7893
 
12.4%
7 37
 
0.1%
9 16
 
< 0.1%
Ù¡ 6
 
< 0.1%
4 5
 
< 0.1%
Ù¢ 3
 
< 0.1%
Other values (3) 9
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
D 7919
50.0%
A 7919
50.0%
Other Punctuation
ValueCountFrequency (%)
. 7973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71663
81.9%
Latin 15838
 
18.1%
Arabic 18
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23708
33.1%
0 16191
22.6%
. 7973
 
11.1%
2 7940
 
11.1%
5 7900
 
11.0%
8 7893
 
11.0%
7 37
 
0.1%
9 16
 
< 0.1%
4 5
 
< 0.1%
Arabic
ValueCountFrequency (%)
Ù¡ 6
33.3%
Ù¢ 3
16.7%
Ù  3
16.7%
Ù¨ 3
16.7%
Ù¥ 3
16.7%
Latin
ValueCountFrequency (%)
D 7919
50.0%
A 7919
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87501
> 99.9%
Arabic 18
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23708
27.1%
0 16191
18.5%
. 7973
 
9.1%
2 7940
 
9.1%
D 7919
 
9.1%
A 7919
 
9.1%
5 7900
 
9.0%
8 7893
 
9.0%
7 37
 
< 0.1%
9 16
 
< 0.1%
Arabic
ValueCountFrequency (%)
Ù¡ 6
33.3%
Ù¢ 3
16.7%
Ù  3
16.7%
Ù¨ 3
16.7%
Ù¥ 3
16.7%

occurred_at_time
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct9718
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2023-06-28 06:42:15
 
3
2023-07-12 18:47:18
 
3
2023-06-27 16:14:22
 
3
2022-08-17 09:21:58
 
3
2023-02-03 22:36:30
 
3
Other values (9713)
9984 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters189981
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9444 ?
Unique (%)94.4%

Sample

1st row2023-04-05 08:54:50
2nd row2022-09-05 18:13:47
3rd row2023-09-10 12:22:06
4th row2022-09-14 07:50:05
5th row2022-09-12 14:12:19

Common Values

ValueCountFrequency (%)
2023-06-28 06:42:15 3
 
< 0.1%
2023-07-12 18:47:18 3
 
< 0.1%
2023-06-27 16:14:22 3
 
< 0.1%
2022-08-17 09:21:58 3
 
< 0.1%
2023-02-03 22:36:30 3
 
< 0.1%
2023-06-20 12:31:43 3
 
< 0.1%
2022-09-28 13:53:29 3
 
< 0.1%
2023-03-28 16:35:30 2
 
< 0.1%
2022-08-16 14:03:25 2
 
< 0.1%
2023-08-28 09:06:32 2
 
< 0.1%
Other values (9708) 9972
99.7%

Length

2023-10-05T19:40:27.064973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-09-06 94
 
0.5%
2023-07-11 78
 
0.4%
2023-09-13 74
 
0.4%
2023-09-14 72
 
0.4%
2023-09-07 69
 
0.3%
2023-06-20 67
 
0.3%
2023-09-12 65
 
0.3%
2023-09-18 65
 
0.3%
2023-08-31 64
 
0.3%
2023-09-11 63
 
0.3%
Other values (9413) 19287
96.4%

Most occurring characters

ValueCountFrequency (%)
2 34758
18.3%
0 31781
16.7%
1 20322
10.7%
- 19998
10.5%
: 19998
10.5%
3 14799
7.8%
9999
 
5.3%
5 7983
 
4.2%
4 7420
 
3.9%
7 5872
 
3.1%
Other values (3) 17051
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 139986
73.7%
Dash Punctuation 19998
 
10.5%
Other Punctuation 19998
 
10.5%
Space Separator 9999
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 34758
24.8%
0 31781
22.7%
1 20322
14.5%
3 14799
10.6%
5 7983
 
5.7%
4 7420
 
5.3%
7 5872
 
4.2%
8 5810
 
4.2%
6 5702
 
4.1%
9 5539
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 19998
100.0%
Other Punctuation
ValueCountFrequency (%)
: 19998
100.0%
Space Separator
ValueCountFrequency (%)
9999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 34758
18.3%
0 31781
16.7%
1 20322
10.7%
- 19998
10.5%
: 19998
10.5%
3 14799
7.8%
9999
 
5.3%
5 7983
 
4.2%
4 7420
 
3.9%
7 5872
 
3.1%
Other values (3) 17051
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 34758
18.3%
0 31781
16.7%
1 20322
10.7%
- 19998
10.5%
: 19998
10.5%
3 14799
7.8%
9999
 
5.3%
5 7983
 
4.2%
4 7420
 
3.9%
7 5872
 
3.1%
Other values (3) 17051
9.0%

app_version
Categorical

Distinct107
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2023.7.6
 
533
2023.8.16
 
471
2023.6.12
 
455
2023.4.21
 
451
2022.7.19
 
415
Other values (102)
7674 

Length

Max length28
Median length9
Mean length11.638064
Min length3

Characters and Unicode

Total characters116369
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st row2023.03.24-3923-prod-release
2nd row2022.8.26
3rd row2023.9.4
4th row2022.08.30-3095-prod-release
5th row2022.8.29

Common Values

ValueCountFrequency (%)
2023.7.6 533
 
5.3%
2023.8.16 471
 
4.7%
2023.6.12 455
 
4.6%
2023.4.21 451
 
4.5%
2022.7.19 415
 
4.2%
2022.8.26 307
 
3.1%
2023.9.4 303
 
3.0%
2023.3.28 285
 
2.9%
2023.5.15 285
 
2.9%
2023.1.11 284
 
2.8%
Other values (97) 6210
62.1%

Length

2023-10-05T19:40:27.294826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023.7.6 533
 
5.3%
2023.8.16 471
 
4.7%
2023.6.12 455
 
4.6%
2023.4.21 451
 
4.5%
2022.7.19 415
 
4.2%
2022.8.26 307
 
3.1%
2023.9.4 303
 
3.0%
2023.3.28 285
 
2.9%
2023.5.15 285
 
2.9%
2023.1.11 284
 
2.8%
Other values (97) 6210
62.1%

Most occurring characters

ValueCountFrequency (%)
2 28319
24.3%
. 19990
17.2%
0 13343
11.5%
3 9230
 
7.9%
1 8634
 
7.4%
- 4313
 
3.7%
e 4309
 
3.7%
6 3467
 
3.0%
8 3068
 
2.6%
9 3050
 
2.6%
Other values (13) 18646
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76245
65.5%
Other Punctuation 19990
 
17.2%
Lowercase Letter 15821
 
13.6%
Dash Punctuation 4313
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4309
27.2%
r 2874
18.2%
d 1443
 
9.1%
o 1439
 
9.1%
p 1439
 
9.1%
l 1435
 
9.1%
a 1435
 
9.1%
s 1435
 
9.1%
b 4
 
< 0.1%
u 4
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 28319
37.1%
0 13343
17.5%
3 9230
 
12.1%
1 8634
 
11.3%
6 3467
 
4.5%
8 3068
 
4.0%
9 3050
 
4.0%
7 2793
 
3.7%
4 2669
 
3.5%
5 1672
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 19990
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4313
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100548
86.4%
Latin 15821
 
13.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 28319
28.2%
. 19990
19.9%
0 13343
13.3%
3 9230
 
9.2%
1 8634
 
8.6%
- 4313
 
4.3%
6 3467
 
3.4%
8 3068
 
3.1%
9 3050
 
3.0%
7 2793
 
2.8%
Other values (2) 4341
 
4.3%
Latin
ValueCountFrequency (%)
e 4309
27.2%
r 2874
18.2%
d 1443
 
9.1%
o 1439
 
9.1%
p 1439
 
9.1%
l 1435
 
9.1%
a 1435
 
9.1%
s 1435
 
9.1%
b 4
 
< 0.1%
u 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 28319
24.3%
. 19990
17.2%
0 13343
11.5%
3 9230
 
7.9%
1 8634
 
7.4%
- 4313
 
3.7%
e 4309
 
3.7%
6 3467
 
3.0%
8 3068
 
2.6%
9 3050
 
2.6%
Other values (13) 18646
16.0%

assist_level
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct7
Distinct (%)0.1%
Missing1798
Missing (%)18.0%
Infinite0
Infinite (%)0.0%
Mean1.8598951
Minimum0
Maximum205
Zeros475
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:27.500108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile2
Maximum205
Range205
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.4874694
Coefficient of variation (CV)4.0257481
Kurtosis599.89668
Mean1.8598951
Median Absolute Deviation (MAD)0
Skewness23.947944
Sum15253
Variance56.062198
MonotonicityNot monotonic
2023-10-05T19:40:27.726226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 4835
48.4%
1 2874
28.7%
0 475
 
4.8%
112 8
 
0.1%
205 6
 
0.1%
191 2
 
< 0.1%
201 1
 
< 0.1%
(Missing) 1798
 
18.0%
ValueCountFrequency (%)
0 475
 
4.8%
1 2874
28.7%
2 4835
48.4%
112 8
 
0.1%
191 2
 
< 0.1%
201 1
 
< 0.1%
205 6
 
0.1%
ValueCountFrequency (%)
205 6
 
0.1%
201 1
 
< 0.1%
191 2
 
< 0.1%
112 8
 
0.1%
2 4835
48.4%
1 2874
28.7%
0 475
 
4.8%

battery_cells
Categorical

HIGH CARDINALITY  MISSING 

Distinct782
Distinct (%)54.8%
Missing8573
Missing (%)85.7%
Memory size78.2 KiB
[null,null,null,null,null,null,null,null,null,null]
620 
[4021,4021,4023,4019,4022,4026,4024,3973,4007,4006]
 
2
[3933,3925,3942,3927,3933,3944,3936,3964,3961,3964]
 
2
[3864,3860,3870,3865,3881,3888,3886,3812,3823,3835]
 
2
[4107,4110,4106,4102,4106,4121,4121,4121,4121,4124]
 
2
Other values (777)
798 

Length

Max length51
Median length51
Mean length50.957924
Min length21

Characters and Unicode

Total characters72666
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique756 ?
Unique (%)53.0%

Sample

1st row[null,null,null,null,null,null,null,null,null,null]
2nd row[3997,3997,4001,3994,4000,4013,4014,4015,4009,4012]
3rd row[4081,4081,4082,4083,4081,4085,4083,4083,4085,4083]
4th row[3913,3911,3911,3911,3911,3923,3921,3914,3923,3922]
5th row[null,null,null,null,null,null,null,null,null,null]

Common Values

ValueCountFrequency (%)
[null,null,null,null,null,null,null,null,null,null] 620
 
6.2%
[4021,4021,4023,4019,4022,4026,4024,3973,4007,4006] 2
 
< 0.1%
[3933,3925,3942,3927,3933,3944,3936,3964,3961,3964] 2
 
< 0.1%
[3864,3860,3870,3865,3881,3888,3886,3812,3823,3835] 2
 
< 0.1%
[4107,4110,4106,4102,4106,4121,4121,4121,4121,4124] 2
 
< 0.1%
[0,0,0,0,0,0,0,0,0,0] 2
 
< 0.1%
[3900,3902,3900,3902,3903,3918,3915,3744,3755,3756] 2
 
< 0.1%
[3814,3816,3811,3814,3815,3824,3823,3821,3820,3820] 2
 
< 0.1%
[4004,4005,4004,4000,4008,4016,4015,4017,4016,4017] 2
 
< 0.1%
[4015,4018,4013,4013,4012,4020,4022,4022,4022,4022] 2
 
< 0.1%
Other values (772) 788
 
7.9%
(Missing) 8573
85.7%

Length

2023-10-05T19:40:27.980676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
null,null,null,null,null,null,null,null,null,null 620
43.5%
4071,4068,4066,4069,4069,4072,4071,4070,4068,4071 2
 
0.1%
3972,3954,3972,3957,3961,3971,3953,4004,3994,3994 2
 
0.1%
3593,4073,4075,4072,4074,4076,4080,4079,4079,4080 2
 
0.1%
3897,3886,3906,3893,3899,3909,3900,3948,3947,3956 2
 
0.1%
3960,3956,3956,3959,3957,3973,3969,3966,3966,3967 2
 
0.1%
4011,4014,4019,4019,4017,4019,4014,4023,4012,4011 2
 
0.1%
3721,3711,3736,3714,3719,3733,3716,3724,3718,3727 2
 
0.1%
3695,3692,3692,3694,3696,3706,3706,3476,3533,3538 2
 
0.1%
4128,4120,4120,4129,4125,4147,4146,4142,4140,4145 2
 
0.1%
Other values (772) 788
55.3%

Most occurring characters

ValueCountFrequency (%)
, 12834
17.7%
l 12508
17.2%
3 6700
9.2%
n 6254
8.6%
u 6254
8.6%
4 4892
 
6.7%
0 3580
 
4.9%
8 2904
 
4.0%
9 2824
 
3.9%
7 2470
 
3.4%
Other values (6) 11446
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31964
44.0%
Lowercase Letter 25016
34.4%
Other Punctuation 12834
17.7%
Open Punctuation 1426
 
2.0%
Close Punctuation 1426
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 6700
21.0%
4 4892
15.3%
0 3580
11.2%
8 2904
9.1%
9 2824
8.8%
7 2470
 
7.7%
1 2460
 
7.7%
6 2357
 
7.4%
5 2168
 
6.8%
2 1609
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
l 12508
50.0%
n 6254
25.0%
u 6254
25.0%
Other Punctuation
ValueCountFrequency (%)
, 12834
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 1426
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1426
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47650
65.6%
Latin 25016
34.4%

Most frequent character per script

Common
ValueCountFrequency (%)
, 12834
26.9%
3 6700
14.1%
4 4892
 
10.3%
0 3580
 
7.5%
8 2904
 
6.1%
9 2824
 
5.9%
7 2470
 
5.2%
1 2460
 
5.2%
6 2357
 
4.9%
5 2168
 
4.5%
Other values (3) 4461
 
9.4%
Latin
ValueCountFrequency (%)
l 12508
50.0%
n 6254
25.0%
u 6254
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 12834
17.7%
l 12508
17.2%
3 6700
9.2%
n 6254
8.6%
u 6254
8.6%
4 4892
 
6.7%
0 3580
 
4.9%
8 2904
 
4.0%
9 2824
 
3.9%
7 2470
 
3.4%
Other values (6) 11446
15.8%

battery_error_state
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.2%
Missing76
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean9601744.3
Minimum0
Maximum2.1810381 × 109
Zeros9872
Zeros (%)98.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:28.712085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.1810381 × 109
Range2.1810381 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4283984 × 108
Coefficient of variation (CV)14.876447
Kurtosis220.36174
Mean9601744.3
Median Absolute Deviation (MAD)0
Skewness14.906269
Sum9.5278109 × 1010
Variance2.040322 × 1016
MonotonicityNot monotonic
2023-10-05T19:40:28.955824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 9872
98.7%
2147615233 10
 
0.1%
2147487776 10
 
0.1%
2147483649 10
 
0.1%
2147485728 5
 
0.1%
205290152 3
 
< 0.1%
2164260864 2
 
< 0.1%
33556784 2
 
< 0.1%
2148663808 2
 
< 0.1%
16779536 2
 
< 0.1%
Other values (5) 5
 
0.1%
(Missing) 76
 
0.8%
ValueCountFrequency (%)
0 9872
98.7%
16779536 2
 
< 0.1%
33556784 2
 
< 0.1%
205290152 3
 
< 0.1%
2147483649 10
 
0.1%
2147485728 5
 
0.1%
2147487776 10
 
0.1%
2147489824 1
 
< 0.1%
2147614720 1
 
< 0.1%
2147614722 1
 
< 0.1%
ValueCountFrequency (%)
2181038080 1
 
< 0.1%
2164260864 2
 
< 0.1%
2148663810 1
 
< 0.1%
2148663808 2
 
< 0.1%
2147615233 10
0.1%
2147614722 1
 
< 0.1%
2147614720 1
 
< 0.1%
2147489824 1
 
< 0.1%
2147487776 10
0.1%
2147485728 5
0.1%

battery_serial_number
Categorical

HIGH CARDINALITY  MISSING 

Distinct4408
Distinct (%)54.6%
Missing1925
Missing (%)19.3%
Memory size78.2 KiB
2VHT02401P02-P4BR522
 
140
BAT_SN
 
125
HT1002MA121520110
 
33
HT1002MA121520478
 
30
3256485430323430315030322d50344252353232
 
27
Other values (4403)
7719 

Length

Max length512
Median length17
Mean length56.547684
Min length6

Characters and Unicode

Total characters456566
Distinct characters45
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3000 ?
Unique (%)37.2%

Sample

1st rowHT1002MA121521183
2nd rowHT1002MA121251821
3rd row4854313030324d41313231353231373236000000
4th rowHT1002MA122120371HT1002MA122120371HT1002MA122120371HT1002MA122120371HT1002MA122120371
5th rowHT1002MA121340143

Common Values

ValueCountFrequency (%)
2VHT02401P02-P4BR522 140
 
1.4%
BAT_SN 125
 
1.3%
HT1002MA121520110 33
 
0.3%
HT1002MA121520478 30
 
0.3%
3256485430323430315030322d50344252353232 27
 
0.3%
HT1002MA122080723 26
 
0.3%
HT1002MA121520980 24
 
0.2%
HT1002MA122080276 20
 
0.2%
HT1002MA121521549 20
 
0.2%
HT1002MA121402126 18
 
0.2%
Other values (4398) 7611
76.1%
(Missing) 1925
 
19.3%

Length

2023-10-05T19:40:29.259116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ht1002ma123240225 166
 
1.8%
123 156
 
1.7%
2vht02401p02-p4br522 140
 
1.5%
bat_sn 125
 
1.3%
12398122long 74
 
0.8%
123ht1002ma32233064long 59
 
0.6%
ht1002ma323310199 50
 
0.5%
123ht1002ma32233096long 40
 
0.4%
ht1002ma322391121 39
 
0.4%
ht1002ma121520110 33
 
0.4%
Other values (4381) 8378
90.5%

Most occurring characters

ValueCountFrequency (%)
2 89442
19.6%
0 87109
19.1%
1 74164
16.2%
3 26261
 
5.8%
T 24962
 
5.5%
H 24803
 
5.4%
A 24758
 
5.4%
M 24560
 
5.4%
4 21645
 
4.7%
5 14242
 
3.1%
Other values (35) 44620
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 350215
76.7%
Uppercase Letter 103239
 
22.6%
Control 1265
 
0.3%
Lowercase Letter 801
 
0.2%
Space Separator 641
 
0.1%
Dash Punctuation 239
 
0.1%
Connector Punctuation 161
 
< 0.1%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 24962
24.2%
H 24803
24.0%
A 24758
24.0%
M 24560
23.8%
N 795
 
0.8%
G 640
 
0.6%
L 636
 
0.6%
O 636
 
0.6%
P 478
 
0.5%
B 408
 
0.4%
Other values (7) 563
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
d 707
88.3%
e 23
 
2.9%
i 12
 
1.5%
t 12
 
1.5%
c 6
 
0.7%
v 6
 
0.7%
n 6
 
0.7%
g 6
 
0.7%
a 6
 
0.7%
r 6
 
0.7%
Other values (2) 11
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 89442
25.5%
0 87109
24.9%
1 74164
21.2%
3 26261
 
7.5%
4 21645
 
6.2%
5 14242
 
4.1%
6 11662
 
3.3%
8 11269
 
3.2%
9 7272
 
2.1%
7 7149
 
2.0%
Control
ValueCountFrequency (%)
633
50.0%
632
50.0%
Space Separator
ValueCountFrequency (%)
641
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 239
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 161
100.0%
Other Punctuation
ValueCountFrequency (%)
¡ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 352526
77.2%
Latin 104040
 
22.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 24962
24.0%
H 24803
23.8%
A 24758
23.8%
M 24560
23.6%
N 795
 
0.8%
d 707
 
0.7%
G 640
 
0.6%
L 636
 
0.6%
O 636
 
0.6%
P 478
 
0.5%
Other values (19) 1065
 
1.0%
Common
ValueCountFrequency (%)
2 89442
25.4%
0 87109
24.7%
1 74164
21.0%
3 26261
 
7.4%
4 21645
 
6.1%
5 14242
 
4.0%
6 11662
 
3.3%
8 11269
 
3.2%
9 7272
 
2.1%
7 7149
 
2.0%
Other values (6) 2311
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456561
> 99.9%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 89442
19.6%
0 87109
19.1%
1 74164
16.2%
3 26261
 
5.8%
T 24962
 
5.5%
H 24803
 
5.4%
A 24758
 
5.4%
M 24560
 
5.4%
4 21645
 
4.7%
5 14242
 
3.1%
Other values (34) 44615
9.8%
None
ValueCountFrequency (%)
¡ 5
100.0%

battery_state_battery_soc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct101
Distinct (%)1.2%
Missing1907
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean67.747158
Minimum0
Maximum100
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:29.566145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26
Q152
median71
Q386
95-th percentile99
Maximum100
Range100
Interquartile range (IQR)34

Descriptive statistics

Standard deviation22.789468
Coefficient of variation (CV)0.33639003
Kurtosis-0.46337
Mean67.747158
Median Absolute Deviation (MAD)17
Skewness-0.55026616
Sum548210
Variance519.35987
MonotonicityNot monotonic
2023-10-05T19:40:29.905625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 295
 
3.0%
99 199
 
2.0%
86 164
 
1.6%
98 159
 
1.6%
82 159
 
1.6%
87 154
 
1.5%
88 151
 
1.5%
85 150
 
1.5%
81 149
 
1.5%
80 144
 
1.4%
Other values (91) 6368
63.7%
(Missing) 1907
 
19.1%
ValueCountFrequency (%)
0 13
0.1%
1 2
 
< 0.1%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 7
0.1%
5 9
0.1%
6 6
0.1%
7 6
0.1%
8 9
0.1%
9 12
0.1%
ValueCountFrequency (%)
100 295
3.0%
99 199
2.0%
98 159
1.6%
97 129
1.3%
96 90
 
0.9%
95 128
1.3%
94 103
 
1.0%
93 127
1.3%
92 96
 
1.0%
91 114
 
1.1%

battery_state_battery_soh
Real number (ℝ)

Distinct25
Distinct (%)0.3%
Missing1907
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean97.368018
Minimum0
Maximum100
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:30.185300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile93
Q197
median98
Q399
95-th percentile99
Maximum100
Range100
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.6096689
Coefficient of variation (CV)0.047342742
Kurtosis342.50145
Mean97.368018
Median Absolute Deviation (MAD)1
Skewness-16.583122
Sum787902
Variance21.249047
MonotonicityNot monotonic
2023-10-05T19:40:30.442108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
99 3110
31.1%
98 1919
19.2%
97 1044
 
10.4%
96 676
 
6.8%
95 368
 
3.7%
100 308
 
3.1%
94 232
 
2.3%
93 141
 
1.4%
92 87
 
0.9%
91 56
 
0.6%
Other values (15) 151
 
1.5%
(Missing) 1907
19.1%
ValueCountFrequency (%)
0 14
0.1%
72 2
 
< 0.1%
78 1
 
< 0.1%
79 2
 
< 0.1%
80 2
 
< 0.1%
81 1
 
< 0.1%
82 4
 
< 0.1%
83 1
 
< 0.1%
84 2
 
< 0.1%
85 4
 
< 0.1%
ValueCountFrequency (%)
100 308
 
3.1%
99 3110
31.1%
98 1919
19.2%
97 1044
 
10.4%
96 676
 
6.8%
95 368
 
3.7%
94 232
 
2.3%
93 141
 
1.4%
92 87
 
0.9%
91 56
 
0.6%

battery_state_cell_temp_1
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct39
Distinct (%)2.8%
Missing8595
Missing (%)86.0%
Infinite0
Infinite (%)0.0%
Mean14.729345
Minimum0
Maximum96
Zeros591
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:30.741152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q326
95-th percentile33
Maximum96
Range96
Interquartile range (IQR)26

Descriptive statistics

Standard deviation13.416598
Coefficient of variation (CV)0.9108754
Kurtosis-0.7973934
Mean14.729345
Median Absolute Deviation (MAD)11
Skewness0.11386607
Sum20680
Variance180.0051
MonotonicityNot monotonic
2023-10-05T19:40:31.058705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 591
 
5.9%
26 72
 
0.7%
27 65
 
0.7%
25 61
 
0.6%
29 58
 
0.6%
23 56
 
0.6%
21 56
 
0.6%
24 49
 
0.5%
28 49
 
0.5%
22 45
 
0.5%
Other values (29) 302
 
3.0%
(Missing) 8595
86.0%
ValueCountFrequency (%)
0 591
5.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
10 6
 
0.1%
11 3
 
< 0.1%
12 5
 
0.1%
13 7
 
0.1%
14 4
 
< 0.1%
ValueCountFrequency (%)
96 1
 
< 0.1%
43 1
 
< 0.1%
42 1
 
< 0.1%
40 1
 
< 0.1%
39 2
 
< 0.1%
38 3
 
< 0.1%
37 2
 
< 0.1%
36 11
0.1%
35 14
0.1%
34 15
0.2%

battery_state_cell_temp_2
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct38
Distinct (%)2.7%
Missing8595
Missing (%)86.0%
Infinite0
Infinite (%)0.0%
Mean14.598291
Minimum0
Maximum90
Zeros592
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:31.532026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q326
95-th percentile32
Maximum90
Range90
Interquartile range (IQR)26

Descriptive statistics

Standard deviation13.287689
Coefficient of variation (CV)0.91022225
Kurtosis-1.014763
Mean14.598291
Median Absolute Deviation (MAD)11
Skewness0.0852262
Sum20496
Variance176.56268
MonotonicityNot monotonic
2023-10-05T19:40:31.974320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 592
 
5.9%
26 69
 
0.7%
25 68
 
0.7%
27 63
 
0.6%
23 58
 
0.6%
22 54
 
0.5%
28 53
 
0.5%
29 49
 
0.5%
31 47
 
0.5%
21 46
 
0.5%
Other values (28) 305
 
3.1%
(Missing) 8595
86.0%
ValueCountFrequency (%)
0 592
5.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
6 1
 
< 0.1%
9 2
 
< 0.1%
10 5
 
0.1%
11 3
 
< 0.1%
12 4
 
< 0.1%
13 8
 
0.1%
14 4
 
< 0.1%
ValueCountFrequency (%)
90 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 2
 
< 0.1%
37 6
 
0.1%
36 8
 
0.1%
35 10
 
0.1%
34 15
0.2%
33 25
0.3%

battery_state_chg_temp
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct40
Distinct (%)2.8%
Missing8595
Missing (%)86.0%
Infinite0
Infinite (%)0.0%
Mean15.878205
Minimum0
Maximum255
Zeros594
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:32.441823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q328
95-th percentile36
Maximum255
Range255
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.726824
Coefficient of variation (CV)0.99046608
Kurtosis36.205172
Mean15.878205
Median Absolute Deviation (MAD)15
Skewness2.5048222
Sum22293
Variance247.33298
MonotonicityNot monotonic
2023-10-05T19:40:32.965995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 594
 
5.9%
25 67
 
0.7%
28 54
 
0.5%
31 49
 
0.5%
27 45
 
0.5%
29 44
 
0.4%
22 44
 
0.4%
30 42
 
0.4%
26 41
 
0.4%
33 38
 
0.4%
Other values (30) 386
 
3.9%
(Missing) 8595
86.0%
ValueCountFrequency (%)
0 594
5.9%
6 1
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
11 7
 
0.1%
12 4
 
< 0.1%
13 2
 
< 0.1%
14 10
 
0.1%
15 6
 
0.1%
ValueCountFrequency (%)
255 1
 
< 0.1%
48 1
 
< 0.1%
47 1
 
< 0.1%
43 3
 
< 0.1%
41 6
 
0.1%
40 11
0.1%
39 9
0.1%
38 15
0.2%
37 19
0.2%
36 21
0.2%

battery_state_dsg_temp
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct40
Distinct (%)2.8%
Missing8595
Missing (%)86.0%
Infinite0
Infinite (%)0.0%
Mean15.881766
Minimum0
Maximum255
Zeros594
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:33.453119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q328
95-th percentile36
Maximum255
Range255
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.72855
Coefficient of variation (CV)0.9903527
Kurtosis36.185787
Mean15.881766
Median Absolute Deviation (MAD)15
Skewness2.5034773
Sum22298
Variance247.38729
MonotonicityNot monotonic
2023-10-05T19:40:33.982443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 594
 
5.9%
25 67
 
0.7%
28 54
 
0.5%
31 49
 
0.5%
27 45
 
0.5%
29 44
 
0.4%
22 44
 
0.4%
24 42
 
0.4%
30 42
 
0.4%
26 41
 
0.4%
Other values (30) 382
 
3.8%
(Missing) 8595
86.0%
ValueCountFrequency (%)
0 594
5.9%
6 1
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
11 7
 
0.1%
12 4
 
< 0.1%
13 2
 
< 0.1%
14 10
 
0.1%
15 6
 
0.1%
ValueCountFrequency (%)
255 1
 
< 0.1%
48 1
 
< 0.1%
47 1
 
< 0.1%
43 3
 
< 0.1%
41 6
 
0.1%
40 11
0.1%
39 9
0.1%
38 15
0.2%
37 19
0.2%
36 21
0.2%

battery_state_full_capacity
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct105
Distinct (%)7.5%
Missing8595
Missing (%)86.0%
Infinite0
Infinite (%)0.0%
Mean5399.4993
Minimum0
Maximum9506
Zeros591
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:34.440149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9134
Q39417
95-th percentile9491
Maximum9506
Range9506
Interquartile range (IQR)9417

Descriptive statistics

Standard deviation4608.0912
Coefficient of variation (CV)0.85342936
Kurtosis-1.8976715
Mean5399.4993
Median Absolute Deviation (MAD)357
Skewness-0.31703031
Sum7580897
Variance21234505
MonotonicityNot monotonic
2023-10-05T19:40:34.991259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 591
 
5.9%
9484 53
 
0.5%
9499 41
 
0.4%
9476 38
 
0.4%
9468 31
 
0.3%
9491 31
 
0.3%
9506 27
 
0.3%
9438 25
 
0.3%
9461 24
 
0.2%
9453 24
 
0.2%
Other values (95) 519
 
5.2%
(Missing) 8595
86.0%
ValueCountFrequency (%)
0 591
5.9%
8176 1
 
< 0.1%
8191 1
 
< 0.1%
8252 1
 
< 0.1%
8366 1
 
< 0.1%
8472 3
 
< 0.1%
8488 1
 
< 0.1%
8526 1
 
< 0.1%
8594 2
 
< 0.1%
8602 2
 
< 0.1%
ValueCountFrequency (%)
9506 27
0.3%
9499 41
0.4%
9491 31
0.3%
9484 53
0.5%
9476 38
0.4%
9468 31
0.3%
9461 24
0.2%
9453 24
0.2%
9446 22
0.2%
9438 25
0.3%

battery_state_pack_voltage
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5450
Distinct (%)67.4%
Missing1907
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean-14984.501
Minimum-32766
Maximum41878
Zeros13
Zeros (%)0.1%
Negative6581
Negative (%)65.8%
Memory size78.2 KiB
2023-10-05T19:40:35.546826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-32766
5-th percentile-30447.45
Q1-28052
median-26050.5
Q3-24538
95-th percentile40512
Maximum41878
Range74644
Interquartile range (IQR)3514

Descriptive statistics

Standard deviation25319.913
Coefficient of variation (CV)-1.6897402
Kurtosis0.70536722
Mean-14984.501
Median Absolute Deviation (MAD)1792.5
Skewness1.6250088
Sum-1.2125458 × 108
Variance6.4109802 × 108
MonotonicityNot monotonic
2023-10-05T19:40:36.107910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4200 38
 
0.4%
0 13
 
0.1%
-24821 7
 
0.1%
-24790 6
 
0.1%
-24777 6
 
0.1%
-24898 6
 
0.1%
-28275 6
 
0.1%
-27111 6
 
0.1%
-24804 6
 
0.1%
-24822 6
 
0.1%
Other values (5440) 7992
79.9%
(Missing) 1907
 
19.1%
ValueCountFrequency (%)
-32766 1
< 0.1%
-32730 1
< 0.1%
-32725 1
< 0.1%
-32610 1
< 0.1%
-32560 1
< 0.1%
-32550 1
< 0.1%
-32549 1
< 0.1%
-32530 1
< 0.1%
-32514 1
< 0.1%
-32507 1
< 0.1%
ValueCountFrequency (%)
41878 1
< 0.1%
41865 1
< 0.1%
41849 1
< 0.1%
41824 1
< 0.1%
41818 1
< 0.1%
41816 1
< 0.1%
41815 1
< 0.1%
41814 1
< 0.1%
41812 1
< 0.1%
41798 1
< 0.1%

battery_state_pre_start_temp
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct40
Distinct (%)2.8%
Missing8595
Missing (%)86.0%
Infinite0
Infinite (%)0.0%
Mean15.885328
Minimum0
Maximum255
Zeros594
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:36.837063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q328
95-th percentile36
Maximum255
Range255
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.730502
Coefficient of variation (CV)0.99025358
Kurtosis36.164213
Mean15.885328
Median Absolute Deviation (MAD)15
Skewness2.5020697
Sum22303
Variance247.44871
MonotonicityNot monotonic
2023-10-05T19:40:37.192011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 594
 
5.9%
25 72
 
0.7%
28 54
 
0.5%
31 49
 
0.5%
27 45
 
0.5%
29 44
 
0.4%
22 44
 
0.4%
30 42
 
0.4%
26 41
 
0.4%
33 38
 
0.4%
Other values (30) 381
 
3.8%
(Missing) 8595
86.0%
ValueCountFrequency (%)
0 594
5.9%
6 1
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
11 7
 
0.1%
12 4
 
< 0.1%
13 2
 
< 0.1%
14 10
 
0.1%
15 6
 
0.1%
ValueCountFrequency (%)
255 1
 
< 0.1%
48 1
 
< 0.1%
47 1
 
< 0.1%
43 3
 
< 0.1%
41 6
 
0.1%
40 11
0.1%
39 9
0.1%
38 15
0.2%
37 19
0.2%
36 21
0.2%
Distinct1696
Distinct (%)21.0%
Missing1907
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean-1952.1338
Minimum-18610
Maximum9252
Zeros13
Zeros (%)0.1%
Negative8072
Negative (%)80.7%
Memory size78.2 KiB
2023-10-05T19:40:37.693925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-18610
5-th percentile-11700.85
Q1-1469.75
median-74
Q3-60
95-th percentile-27
Maximum9252
Range27862
Interquartile range (IQR)1409.75

Descriptive statistics

Standard deviation3937.3694
Coefficient of variation (CV)-2.0169567
Kurtosis3.7772679
Mean-1952.1338
Median Absolute Deviation (MAD)19
Skewness-2.1826016
Sum-15796667
Variance15502878
MonotonicityNot monotonic
2023-10-05T19:40:38.009460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-63 664
 
6.6%
-60 614
 
6.1%
-65 549
 
5.5%
-57 406
 
4.1%
-68 393
 
3.9%
-71 346
 
3.5%
-74 241
 
2.4%
-76 167
 
1.7%
-93 154
 
1.5%
-96 153
 
1.5%
Other values (1686) 4405
44.1%
(Missing) 1907
19.1%
ValueCountFrequency (%)
-18610 1
< 0.1%
-17616 1
< 0.1%
-17528 1
< 0.1%
-16907 1
< 0.1%
-16693 1
< 0.1%
-16487 1
< 0.1%
-16473 1
< 0.1%
-16341 1
< 0.1%
-16240 1
< 0.1%
-16234 1
< 0.1%
ValueCountFrequency (%)
9252 1
 
< 0.1%
5654 1
 
< 0.1%
74 1
 
< 0.1%
13 1
 
< 0.1%
2 3
 
< 0.1%
0 13
0.1%
-2 1
 
< 0.1%
-5 1
 
< 0.1%
-8 1
 
< 0.1%
-9 1
 
< 0.1%

battery_state_remaining_capacity
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct734
Distinct (%)52.3%
Missing8595
Missing (%)86.0%
Infinite0
Infinite (%)0.0%
Mean3817.8611
Minimum0
Maximum9498
Zeros591
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:38.320760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3897
Q37315.25
95-th percentile9128.7
Maximum9498
Range9498
Interquartile range (IQR)7315.25

Descriptive statistics

Standard deviation3624.0034
Coefficient of variation (CV)0.94922347
Kurtosis-1.6721612
Mean3817.8611
Median Absolute Deviation (MAD)3897
Skewness0.12921647
Sum5360277
Variance13133401
MonotonicityNot monotonic
2023-10-05T19:40:38.617351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 591
 
5.9%
8250 5
 
0.1%
6450 4
 
< 0.1%
8567 4
 
< 0.1%
9445 4
 
< 0.1%
7685 3
 
< 0.1%
7287 3
 
< 0.1%
9162 3
 
< 0.1%
9005 2
 
< 0.1%
4731 2
 
< 0.1%
Other values (724) 783
 
7.8%
(Missing) 8595
86.0%
ValueCountFrequency (%)
0 591
5.9%
164 1
 
< 0.1%
404 1
 
< 0.1%
507 1
 
< 0.1%
646 1
 
< 0.1%
943 1
 
< 0.1%
976 1
 
< 0.1%
991 1
 
< 0.1%
1066 1
 
< 0.1%
1150 1
 
< 0.1%
ValueCountFrequency (%)
9498 1
 
< 0.1%
9488 1
 
< 0.1%
9483 2
< 0.1%
9467 1
 
< 0.1%
9445 4
< 0.1%
9441 1
 
< 0.1%
9437 2
< 0.1%
9434 1
 
< 0.1%
9429 1
 
< 0.1%
9425 1
 
< 0.1%

dance_area
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
PARIS
5073 
BERLIN
2821 
HAMBURG
1378 
MUNICH
 
392
VIENNA
 
331

Length

Max length11
Median length5
Mean length5.6324632
Min length5

Characters and Unicode

Total characters56319
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARIS
2nd rowPARIS
3rd rowBERLIN
4th rowBERLIN
5th rowHAMBURG

Common Values

ValueCountFrequency (%)
PARIS 5073
50.7%
BERLIN 2821
28.2%
HAMBURG 1378
 
13.8%
MUNICH 392
 
3.9%
VIENNA 331
 
3.3%
UNALLOCATED 4
 
< 0.1%

Length

2023-10-05T19:40:38.921247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T19:40:39.252854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
paris 5073
50.7%
berlin 2821
28.2%
hamburg 1378
 
13.8%
munich 392
 
3.9%
vienna 331
 
3.3%
unallocated 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 9272
16.5%
I 8617
15.3%
A 6790
12.1%
P 5073
9.0%
S 5073
9.0%
B 4199
7.5%
N 3879
6.9%
E 3156
 
5.6%
L 2829
 
5.0%
U 1774
 
3.1%
Other values (8) 5657
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 56319
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 9272
16.5%
I 8617
15.3%
A 6790
12.1%
P 5073
9.0%
S 5073
9.0%
B 4199
7.5%
N 3879
6.9%
E 3156
 
5.6%
L 2829
 
5.0%
U 1774
 
3.1%
Other values (8) 5657
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56319
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 9272
16.5%
I 8617
15.3%
A 6790
12.1%
P 5073
9.0%
S 5073
9.0%
B 4199
7.5%
N 3879
6.9%
E 3156
 
5.6%
L 2829
 
5.0%
U 1774
 
3.1%
Other values (8) 5657
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56319
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 9272
16.5%
I 8617
15.3%
A 6790
12.1%
P 5073
9.0%
S 5073
9.0%
B 4199
7.5%
N 3879
6.9%
E 3156
 
5.6%
L 2829
 
5.0%
U 1774
 
3.1%
Other values (8) 5657
10.0%

event
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Vehicle Connected
9999 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters169983
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVehicle Connected
2nd rowVehicle Connected
3rd rowVehicle Connected
4th rowVehicle Connected
5th rowVehicle Connected

Common Values

ValueCountFrequency (%)
Vehicle Connected 9999
100.0%

Length

2023-10-05T19:40:39.504488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T19:40:39.761629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
vehicle 9999
50.0%
connected 9999
50.0%

Most occurring characters

ValueCountFrequency (%)
e 39996
23.5%
c 19998
11.8%
n 19998
11.8%
V 9999
 
5.9%
h 9999
 
5.9%
i 9999
 
5.9%
l 9999
 
5.9%
9999
 
5.9%
C 9999
 
5.9%
o 9999
 
5.9%
Other values (2) 19998
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139986
82.4%
Uppercase Letter 19998
 
11.8%
Space Separator 9999
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 39996
28.6%
c 19998
14.3%
n 19998
14.3%
h 9999
 
7.1%
i 9999
 
7.1%
l 9999
 
7.1%
o 9999
 
7.1%
t 9999
 
7.1%
d 9999
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
V 9999
50.0%
C 9999
50.0%
Space Separator
ValueCountFrequency (%)
9999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 159984
94.1%
Common 9999
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 39996
25.0%
c 19998
12.5%
n 19998
12.5%
V 9999
 
6.2%
h 9999
 
6.2%
i 9999
 
6.2%
l 9999
 
6.2%
C 9999
 
6.2%
o 9999
 
6.2%
t 9999
 
6.2%
Common
ValueCountFrequency (%)
9999
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 39996
23.5%
c 19998
11.8%
n 19998
11.8%
V 9999
 
5.9%
h 9999
 
5.9%
i 9999
 
5.9%
l 9999
 
5.9%
9999
 
5.9%
C 9999
 
5.9%
o 9999
 
5.9%
Other values (2) 19998
11.8%

hmi_serial_number
Categorical

HIGH CARDINALITY  MISSING 

Distinct3589
Distinct (%)39.7%
Missing962
Missing (%)9.6%
Memory size78.2 KiB
E96F7C
 
36
F1F5B5
 
35
1C25B6
 
35
CA24C6
 
31
EF1522
 
30
Other values (3584)
8870 

Length

Max length32
Median length6
Mean length6.9864999
Min length6

Characters and Unicode

Total characters63137
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1673 ?
Unique (%)18.5%

Sample

1st row60968A
2nd row2C139F
3rd row73D58B
4th row22491134
5th row89F0D3

Common Values

ValueCountFrequency (%)
E96F7C 36
 
0.4%
F1F5B5 35
 
0.4%
1C25B6 35
 
0.4%
CA24C6 31
 
0.3%
EF1522 30
 
0.3%
8433D3 27
 
0.3%
BC86A0 26
 
0.3%
A3A8CB 25
 
0.3%
5B706B 24
 
0.2%
4AAFEB 23
 
0.2%
Other values (3579) 8745
87.5%
(Missing) 962
 
9.6%

Length

2023-10-05T19:40:39.993681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e96f7c 36
 
0.4%
1c25b6 35
 
0.4%
f1f5b5 35
 
0.4%
ca24c6 31
 
0.3%
ef1522 30
 
0.3%
8433d3 27
 
0.3%
bc86a0 26
 
0.3%
a3a8cb 25
 
0.3%
5b706b 24
 
0.3%
4aafeb 23
 
0.3%
Other values (3579) 8745
96.8%

Most occurring characters

ValueCountFrequency (%)
2 8218
13.0%
3 6628
10.5%
4 5974
9.5%
1 5295
 
8.4%
0 5037
 
8.0%
5 4379
 
6.9%
9 4139
 
6.6%
6 3450
 
5.5%
7 3206
 
5.1%
8 2924
 
4.6%
Other values (8) 13887
22.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49250
78.0%
Uppercase Letter 13883
 
22.0%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8218
16.7%
3 6628
13.5%
4 5974
12.1%
1 5295
10.8%
0 5037
10.2%
5 4379
8.9%
9 4139
8.4%
6 3450
7.0%
7 3206
 
6.5%
8 2924
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
B 2531
18.2%
C 2310
16.6%
F 2283
16.4%
E 2272
16.4%
D 2239
16.1%
A 2239
16.1%
N 9
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49250
78.0%
Latin 13887
 
22.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8218
16.7%
3 6628
13.5%
4 5974
12.1%
1 5295
10.8%
0 5037
10.2%
5 4379
8.9%
9 4139
8.4%
6 3450
7.0%
7 3206
 
6.5%
8 2924
 
5.9%
Latin
ValueCountFrequency (%)
B 2531
18.2%
C 2310
16.6%
F 2283
16.4%
E 2272
16.4%
D 2239
16.1%
A 2239
16.1%
N 9
 
0.1%
e 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8218
13.0%
3 6628
10.5%
4 5974
9.5%
1 5295
 
8.4%
0 5037
 
8.0%
5 4379
 
6.9%
9 4139
 
6.6%
6 3450
 
5.5%
7 3206
 
5.1%
8 2924
 
4.6%
Other values (8) 13887
22.0%

os_name
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
iOS
8559 
Android
1439 
iPadOS
 
1

Length

Max length7
Median length3
Mean length3.5759576
Min length3

Characters and Unicode

Total characters35756
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAndroid
2nd rowiOS
3rd rowiOS
4th rowAndroid
5th rowiOS

Common Values

ValueCountFrequency (%)
iOS 8559
85.6%
Android 1439
 
14.4%
iPadOS 1
 
< 0.1%

Length

2023-10-05T19:40:40.295479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T19:40:40.573173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ios 8559
85.6%
android 1439
 
14.4%
ipados 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 9999
28.0%
O 8560
23.9%
S 8560
23.9%
d 2879
 
8.1%
A 1439
 
4.0%
n 1439
 
4.0%
r 1439
 
4.0%
o 1439
 
4.0%
P 1
 
< 0.1%
a 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18560
51.9%
Lowercase Letter 17196
48.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9999
58.1%
d 2879
 
16.7%
n 1439
 
8.4%
r 1439
 
8.4%
o 1439
 
8.4%
a 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
O 8560
46.1%
S 8560
46.1%
A 1439
 
7.8%
P 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 35756
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9999
28.0%
O 8560
23.9%
S 8560
23.9%
d 2879
 
8.1%
A 1439
 
4.0%
n 1439
 
4.0%
r 1439
 
4.0%
o 1439
 
4.0%
P 1
 
< 0.1%
a 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9999
28.0%
O 8560
23.9%
S 8560
23.9%
d 2879
 
8.1%
A 1439
 
4.0%
n 1439
 
4.0%
r 1439
 
4.0%
o 1439
 
4.0%
P 1
 
< 0.1%
a 1
 
< 0.1%

remaining_mileage
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct258
Distinct (%)3.1%
Missing1739
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean561.40266
Minimum0
Maximum64131
Zeros65
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-10-05T19:40:40.838631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile162
Q1342
median472
Q3588
95-th percentile800
Maximum64131
Range64131
Interquartile range (IQR)246

Descriptive statistics

Standard deviation1668.7024
Coefficient of variation (CV)2.9723806
Kurtosis893.06205
Mean561.40266
Median Absolute Deviation (MAD)122
Skewness28.480815
Sum4637186
Variance2784567.7
MonotonicityNot monotonic
2023-10-05T19:40:41.161558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 218
 
2.2%
528 135
 
1.4%
432 133
 
1.3%
504 128
 
1.3%
480 121
 
1.2%
594 121
 
1.2%
408 104
 
1.0%
492 103
 
1.0%
384 100
 
1.0%
456 98
 
1.0%
Other values (248) 6999
70.0%
(Missing) 1739
 
17.4%
ValueCountFrequency (%)
0 65
0.7%
8 2
 
< 0.1%
16 1
 
< 0.1%
18 1
 
< 0.1%
24 2
 
< 0.1%
32 1
 
< 0.1%
36 1
 
< 0.1%
40 7
 
0.1%
48 6
 
0.1%
54 3
 
< 0.1%
ValueCountFrequency (%)
64131 1
< 0.1%
62145 1
< 0.1%
52705 1
< 0.1%
50429 1
< 0.1%
44688 1
< 0.1%
43231 1
< 0.1%
42770 1
< 0.1%
35227 1
< 0.1%
27373 1
< 0.1%
27177 1
< 0.1%

versions_battery_firmware
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct15
Distinct (%)0.2%
Missing2026
Missing (%)20.3%
Memory size78.2 KiB
1.0.6.0
4031 
0.3.3.0
1963 
1.0.3.0
1074 
0.3.2.0
491 
1.0.2.0
 
205
Other values (10)
 
209

Length

Max length11
Median length7
Mean length7.0016305
Min length7

Characters and Unicode

Total characters55824
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row0.3.3.0
2nd row0.3.3.0
3rd row1.0.6.0
4th row1.0.6.0
5th row1.0.6.0

Common Values

ValueCountFrequency (%)
1.0.6.0 4031
40.3%
0.3.3.0 1963
19.6%
1.0.3.0 1074
 
10.7%
0.3.2.0 491
 
4.9%
1.0.2.0 205
 
2.1%
0.0.0.0 141
 
1.4%
0.3.1.0 31
 
0.3%
1.0.1.0 17
 
0.2%
0.2.2.0 8
 
0.1%
0.2.6.0 5
 
0.1%
Other values (5) 7
 
0.1%
(Missing) 2026
20.3%

Length

2023-10-05T19:40:41.496332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1.0.6.0 4031
50.6%
0.3.3.0 1963
24.6%
1.0.3.0 1074
 
13.5%
0.3.2.0 491
 
6.2%
1.0.2.0 205
 
2.6%
0.0.0.0 141
 
1.8%
0.3.1.0 31
 
0.4%
1.0.1.0 17
 
0.2%
0.2.2.0 8
 
0.1%
0.2.6.0 5
 
0.1%
Other values (5) 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 23919
42.8%
0 16226
29.1%
3 5522
 
9.9%
1 5379
 
9.6%
6 4036
 
7.2%
2 720
 
1.3%
5 11
 
< 0.1%
4 8
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31905
57.2%
Other Punctuation 23919
42.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16226
50.9%
3 5522
 
17.3%
1 5379
 
16.9%
6 4036
 
12.7%
2 720
 
2.3%
5 11
 
< 0.1%
4 8
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 23919
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55824
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23919
42.8%
0 16226
29.1%
3 5522
 
9.9%
1 5379
 
9.6%
6 4036
 
7.2%
2 720
 
1.3%
5 11
 
< 0.1%
4 8
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55824
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23919
42.8%
0 16226
29.1%
3 5522
 
9.9%
1 5379
 
9.6%
6 4036
 
7.2%
2 720
 
1.3%
5 11
 
< 0.1%
4 8
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%

versions_hmi_firmware
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)0.1%
Missing2026
Missing (%)20.3%
Memory size78.2 KiB
65.1.39.34
2305 
1.7.18.0
1890 
1.7.28.0
1604 
1.2.18.0
1419 
1.2.28.0
739 
Other values (4)
 
16

Length

Max length10
Median length8
Mean length8.5812116
Min length8

Characters and Unicode

Total characters68418
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.7.18.0
2nd row1.7.18.0
3rd row1.7.28.0
4th row65.1.39.34
5th row65.1.39.34

Common Values

ValueCountFrequency (%)
65.1.39.34 2305
23.1%
1.7.18.0 1890
18.9%
1.7.28.0 1604
16.0%
1.2.18.0 1419
14.2%
1.2.28.0 739
 
7.4%
65.1.20.34 9
 
0.1%
1.7.20.0 4
 
< 0.1%
65.1.21.34 2
 
< 0.1%
65.1.27.34 1
 
< 0.1%
(Missing) 2026
20.3%

Length

2023-10-05T19:40:41.823304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T19:40:42.178699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
65.1.39.34 2305
28.9%
1.7.18.0 1890
23.7%
1.7.28.0 1604
20.1%
1.2.18.0 1419
17.8%
1.2.28.0 739
 
9.3%
65.1.20.34 9
 
0.1%
1.7.20.0 4
 
0.1%
65.1.21.34 2
 
< 0.1%
65.1.27.34 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 23919
35.0%
1 11284
16.5%
0 5669
 
8.3%
8 5652
 
8.3%
3 4622
 
6.8%
2 4517
 
6.6%
7 3499
 
5.1%
6 2317
 
3.4%
5 2317
 
3.4%
4 2317
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44499
65.0%
Other Punctuation 23919
35.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11284
25.4%
0 5669
12.7%
8 5652
12.7%
3 4622
10.4%
2 4517
10.2%
7 3499
 
7.9%
6 2317
 
5.2%
5 2317
 
5.2%
4 2317
 
5.2%
9 2305
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 23919
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23919
35.0%
1 11284
16.5%
0 5669
 
8.3%
8 5652
 
8.3%
3 4622
 
6.8%
2 4517
 
6.6%
7 3499
 
5.1%
6 2317
 
3.4%
5 2317
 
3.4%
4 2317
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23919
35.0%
1 11284
16.5%
0 5669
 
8.3%
8 5652
 
8.3%
3 4622
 
6.8%
2 4517
 
6.6%
7 3499
 
5.1%
6 2317
 
3.4%
5 2317
 
3.4%
4 2317
 
3.4%

versions_hmi_hardware
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2026
Missing (%)20.3%
Memory size78.2 KiB
1.0
3498 
2.0
2317 
0.0
2158 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23919
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 3498
35.0%
2.0 2317
23.2%
0.0 2158
21.6%
(Missing) 2026
20.3%

Length

2023-10-05T19:40:42.497092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T19:40:42.756579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3498
43.9%
2.0 2317
29.1%
0.0 2158
27.1%

Most occurring characters

ValueCountFrequency (%)
0 10131
42.4%
. 7973
33.3%
1 3498
 
14.6%
2 2317
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15946
66.7%
Other Punctuation 7973
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10131
63.5%
1 3498
 
21.9%
2 2317
 
14.5%
Other Punctuation
ValueCountFrequency (%)
. 7973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23919
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10131
42.4%
. 7973
33.3%
1 3498
 
14.6%
2 2317
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10131
42.4%
. 7973
33.3%
1 3498
 
14.6%
2 2317
 
9.7%

Interactions

2023-10-05T19:40:16.312654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:11.117680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:16.580753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:21.184176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:25.367510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:29.769207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:35.303060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:39.681111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:43.442050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:49.242522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:53.781613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:57.613377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:02.503178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:07.353115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:12.127080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:16.731981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:11.393642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:16.975526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:21.430909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:25.633458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:30.177456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:35.951843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:39.916131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:43.685442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:49.680741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:54.025519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:57.864925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:02.903052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:08.028290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:12.367557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:17.162669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:11.614957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:17.375428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:21.675189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:25.864199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:30.594122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:36.230228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:40.199854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:44.018312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:49.959124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:54.301002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:58.123796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:03.307221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:08.260175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:12.638448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:17.583739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:11.848219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:17.817587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:21.957655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:26.121742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:31.019990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:36.505080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:40.441189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:44.331424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:50.718253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:54.539835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:58.375149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:03.692111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:08.496911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:12.904116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:18.037391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:12.095765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:18.190173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:22.219769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:26.362968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:31.434017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:36.758996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:40.703218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:44.750114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:50.981433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:54.812674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:58.644065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:04.124422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:08.746401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:13.177947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:18.363487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:12.390632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:18.529804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:22.476463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:26.610498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:31.789198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:37.046800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:40.956674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:45.155540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:51.263442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:55.093848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:58.891153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:04.541918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:09.025224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:13.432113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:18.756968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:12.737860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:18.806427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:22.741106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:26.871952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:32.233538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:37.302500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:41.226882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:45.569449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:51.519938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:55.363744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:59.139719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:04.886895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:09.255808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:13.681289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:19.623152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:13.018507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:19.065981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:23.014765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:27.142282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:32.656783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:37.563600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:41.464435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:45.987760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:51.775733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:55.619089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:59.372921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:05.258973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:09.479634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:13.933307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:20.078241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:13.711904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:19.325723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:23.273129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:27.406460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:33.036778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:37.830860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:41.741152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:46.371188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:52.038094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:55.862512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:59.644000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:05.597168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:09.753124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:14.227948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:20.482100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:14.121246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:19.590845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:23.829836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:27.663436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:33.471791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:38.101955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:41.992383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:46.786703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:52.292191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:56.125101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:00.031291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:05.868970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:10.183527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:14.483326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:20.874669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:14.521153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:19.862136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:24.085320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:27.930215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:33.916563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:38.347935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:42.232172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:47.212084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:52.531157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:56.371867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:00.423878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:06.114576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:10.418486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:14.733334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:21.298674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:14.975682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:20.135758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:24.352967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:28.240960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:34.254574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:38.616646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:42.469197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:47.606585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:52.763314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:56.614022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:00.864649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:06.356361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:10.839571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:14.978976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:21.585054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:15.343137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:20.398526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:24.591581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:28.527328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:34.523813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:38.872568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:42.711330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:47.989641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:53.025433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:56.851008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:01.261857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:06.598925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:11.160676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:15.247006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:21.862258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:15.760838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:20.654395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:24.841925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:28.934523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:34.789994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:39.136093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:42.944272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:48.341245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:53.280528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:57.108989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:01.703459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:06.853295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:11.410995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:15.510471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:22.139568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:16.153495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:20.921269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:25.115901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:29.339098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:35.042188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:39.394552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:43.174825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:48.790807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:53.509672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:39:57.338174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:02.099113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:07.097008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:11.836111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T19:40:15.885358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-05T19:40:42.990278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
total_mileageassist_levelbattery_error_statebattery_state_battery_socbattery_state_battery_sohbattery_state_cell_temp_1battery_state_cell_temp_2battery_state_chg_tempbattery_state_dsg_tempbattery_state_full_capacitybattery_state_pack_voltagebattery_state_pre_start_tempbattery_state_real_time_currentbattery_state_remaining_capacityremaining_mileageversions_mc_firmwaredance_areaos_nameversions_battery_firmwareversions_hmi_firmwareversions_hmi_hardware
total_mileage1.0000.020-0.081-0.031-0.350-0.137-0.139-0.148-0.149-0.273-0.033-0.1500.066-0.1540.0030.0000.0000.0000.0000.0000.047
assist_level0.0201.000-0.0810.002-0.0420.0440.0430.0590.058-0.004-0.0670.057-0.1110.040-0.3830.0000.0000.0140.0000.0860.049
battery_error_state-0.081-0.0811.0000.0260.021-0.025-0.028-0.024-0.023-0.0260.045-0.0220.087-0.015-0.0780.6330.0000.0000.1940.0150.028
battery_state_battery_soc-0.0310.0020.0261.0000.025-0.023-0.023-0.021-0.0210.0060.678-0.0200.1280.3260.8230.0810.0130.0660.0500.0150.026
battery_state_battery_soh-0.350-0.0420.0210.0251.0000.0040.0080.0100.0110.3520.0110.012-0.0070.0740.0120.2070.0500.0280.1330.0230.021
battery_state_cell_temp_1-0.1370.044-0.025-0.0230.0041.0000.9970.9830.9830.776-0.0350.982-0.2110.784-0.0330.0000.0591.0000.3930.4560.475
battery_state_cell_temp_2-0.1390.043-0.028-0.0230.0080.9971.0000.9820.9820.777-0.0370.982-0.2120.783-0.0340.0000.0581.0000.3910.4530.473
battery_state_chg_temp-0.1480.059-0.024-0.0210.0100.9830.9821.0001.0000.778-0.0451.000-0.2240.783-0.0390.0000.0791.0000.3970.4660.402
battery_state_dsg_temp-0.1490.058-0.023-0.0210.0110.9830.9821.0001.0000.778-0.0461.000-0.2240.783-0.0390.0000.0791.0000.3970.4660.402
battery_state_full_capacity-0.273-0.004-0.0260.0060.3520.7760.7770.7780.7781.000-0.0290.778-0.1770.816-0.0010.0390.0711.0000.6330.7030.454
battery_state_pack_voltage-0.033-0.0670.0450.6780.011-0.035-0.037-0.045-0.046-0.0291.000-0.0470.2270.2800.5780.4000.0270.9850.1960.0520.071
battery_state_pre_start_temp-0.1500.057-0.022-0.0200.0120.9820.9821.0001.0000.778-0.0471.000-0.2230.783-0.0400.0000.0791.0000.3970.4660.402
battery_state_real_time_current0.066-0.1110.0870.128-0.007-0.211-0.212-0.224-0.224-0.1770.227-0.2231.000-0.1600.1330.0000.0300.0160.0000.0380.086
battery_state_remaining_capacity-0.1540.040-0.0150.3260.0740.7840.7830.7830.7830.8160.2800.783-0.1601.0000.2480.0000.0601.0000.3050.4040.446
remaining_mileage0.003-0.383-0.0780.8230.012-0.033-0.034-0.039-0.039-0.0010.578-0.0400.1330.2481.0000.0000.0000.0000.0000.0000.032
versions_mc_firmware0.0000.0000.6330.0810.2070.0000.0000.0000.0000.0390.4000.0000.0000.0000.0001.0000.0390.0380.1900.0350.052
dance_area0.0000.0000.0000.0130.0500.0590.0580.0790.0790.0710.0270.0790.0300.0600.0000.0391.0000.0340.1770.2050.298
os_name0.0000.0140.0000.0660.0281.0001.0001.0001.0001.0000.9851.0000.0161.0000.0000.0380.0341.0000.0950.1050.079
versions_battery_firmware0.0000.0000.1940.0500.1330.3930.3910.3970.3970.6330.1960.3970.0000.3050.0000.1900.1770.0951.0000.5860.405
versions_hmi_firmware0.0000.0860.0150.0150.0230.4560.4530.4660.4660.7030.0520.4660.0380.4040.0000.0350.2050.1050.5861.0001.000
versions_hmi_hardware0.0470.0490.0280.0260.0210.4750.4730.4020.4020.4540.0710.4020.0860.4460.0320.0520.2980.0790.4051.0001.000

Missing values

2023-10-05T19:40:22.624048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-05T19:40:23.554386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-05T19:40:24.444055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

vehicle_iduser_idtotal_mileageversions_mc_firmwareoccurred_at_timeapp_versionassist_levelbattery_cellsbattery_error_statebattery_serial_numberbattery_state_battery_socbattery_state_battery_sohbattery_state_cell_temp_1battery_state_cell_temp_2battery_state_chg_tempbattery_state_dsg_tempbattery_state_full_capacitybattery_state_pack_voltagebattery_state_pre_start_tempbattery_state_real_time_currentbattery_state_remaining_capacitydance_areaeventhmi_serial_numberos_nameremaining_mileageversions_battery_firmwareversions_hmi_firmwareversions_hmi_hardware
0bc3412f7-37a8-4676-8043-53f4a1bb316aca73adbe-5930-45ba-af74-de5865d8b0937281.0DA210815.102023-04-05 08:54:502023.03.24-3923-prod-release0.0[null,null,null,null,null,null,null,null,null,null]0.0HT1002MA12152118365.098.00.00.00.00.00.038925.00.0-60.00.0PARISVehicle Connected60968AAndroid1300.00.3.3.01.7.18.01.0
195e7ee01-3987-481a-9000-3b331c52192f6560e385-b90d-42be-b977-5cdb3a5d5a60NaNNaN2022-09-05 18:13:472022.8.26NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPARISVehicle ConnectedNaNiOSNaNNaNNaNNaN
2c63d60f2-5e2a-483c-bb11-935c45ebf2f0118047dc-bf44-4dc0-9862-0cb3ac5db65d1185.0DA210815.102023-09-10 12:22:062023.9.41.0NaN0.0NaN89.094.0NaNNaNNaNNaNNaN-24763.0NaN-30.0NaNBERLINVehicle Connected2C139FiOS712.00.3.3.01.7.18.01.0
379df825d-8115-4a84-9aa8-87ae307ae561f0c0ac6d-32a1-45ed-9de1-466455fa637a378.0DA210815.102022-09-14 07:50:052022.08.30-3095-prod-release2.0[3997,3997,4001,3994,4000,4013,4014,4015,4009,4012]0.0HT1002MA12125182179.099.017.017.017.017.09468.040052.017.0-107.07479.0BERLINVehicle Connected73D58BAndroid474.01.0.6.01.7.28.01.0
40d30c889-a102-4bdc-b8a3-c1e0d6c536d52b9f8b3f-b3f3-409b-ade0-dd9ae8b54ba7NaNNaN2022-09-12 14:12:192022.8.29NaNNaN0.04854313030324d41313231353231373236000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHAMBURGVehicle ConnectedNaNiOSNaNNaNNaNNaN
55c1e2a7b-afc5-482d-96d0-12c2f938e712a3afa394-cf40-450a-b323-a1d23d685f38NaNNaN2022-09-11 17:14:152022.8.29NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPARISVehicle ConnectedNaNiOSNaNNaNNaNNaN
62d31a77f-26ad-4f92-bd9f-a44d2ad79c898c169cd9-274d-4025-9dd9-bc762f8b385d1135.0DA210815.102023-07-26 07:00:222023.7.202.0NaN0.0HT1002MA122120371HT1002MA122120371HT1002MA122120371HT1002MA122120371HT1002MA12212037195.099.0NaNNaNNaNNaNNaN-24776.0NaN-2603.0NaNPARISVehicle Connected22491134iOS570.01.0.6.065.1.39.342.0
7a53f881c-af7b-4f32-bf8d-13eef013d4b14fe83c19-4de1-4dd0-97dc-4e5ae5850b2bNaNNaN2022-11-05 07:16:312022.10.11NaNNaN0.0HT1002MA121340143NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMUNICHVehicle Connected89F0D3iOSNaNNaNNaNNaN
8d2ce3fbd-f352-42ce-bf6d-19abcac8db32dd798684-8ff6-422c-9c42-a05b70dc6140323.0DA210815.102023-08-08 12:45:072023.08.04-4184-prod-release1.0[4081,4081,4082,4083,4081,4085,4083,4083,4085,4083]0.0HT1002MA1224400492.099.025.025.025.025.09491.040829.025.0-96.08700.0PARISVehicle Connected23020758Android736.01.0.6.065.1.39.342.0
9c88d33c5-c6a6-421a-9deb-9722c36b16176dcaf0ea-3b9d-4666-bd04-9661ce79cc4411254.0DA210815.102023-07-17 13:58:292023.07.06-4135-prod-release2.0[3913,3911,3911,3911,3911,3923,3921,3914,3923,3922]0.0HT1002MA1214015668.091.033.032.034.034.08693.039164.034.0-96.05901.0BERLINVehicle Connected22510490Android408.00.3.3.065.1.39.342.0
vehicle_iduser_idtotal_mileageversions_mc_firmwareoccurred_at_timeapp_versionassist_levelbattery_cellsbattery_error_statebattery_serial_numberbattery_state_battery_socbattery_state_battery_sohbattery_state_cell_temp_1battery_state_cell_temp_2battery_state_chg_tempbattery_state_dsg_tempbattery_state_full_capacitybattery_state_pack_voltagebattery_state_pre_start_tempbattery_state_real_time_currentbattery_state_remaining_capacitydance_areaeventhmi_serial_numberos_nameremaining_mileageversions_battery_firmwareversions_hmi_firmwareversions_hmi_hardware
998961a771fd-f11b-4144-8842-73ebd69471b804445577-ab03-46db-bd07-744d3c58f71c105.0DA210815.102023-08-31 16:16:552023.8.161.0NaN0.0HT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\nHT1002MA323310199\r\n82.0100.0NaNNaNNaNNaNNaN-25304.0NaN-74.0NaNPARISVehicle Connected23070809iOS656.01.0.6.065.1.39.342.0
999074015cd1-8b8d-40f2-92bd-9f7f17fd88312bec0841-17cf-4404-ae1c-852e5e3755532806.0DA210815.102022-10-20 12:00:332022.10.122.0NaN0.0HT1002MA12140203920.098.0NaNNaNNaNNaNNaN-30679.0NaN-65.0NaNPARISVehicle Connected87B451iOS120.00.3.2.01.7.18.01.0
99916eca4ad4-0aca-443a-954d-9983a9d1a7b0725e1583-c2c3-47e3-ab91-3d2a6136e4b22431.0DA210815.102023-05-01 18:32:532023.4.212.0NaN0.0HT1002MA12226071334.095.0NaNNaNNaNNaNNaN-29721.0NaN-6715.0NaNPARISVehicle ConnectedF179D7iOS204.01.0.3.01.7.18.01.0
9992ca4c5d0d-0d08-4189-b8ee-e8cd4d3374227d7c1e29-1a35-447c-b672-5899a55bc6f112411.0DA210815.102022-08-30 18:59:022022.8.262.0NaN0.04854313030324d4131323230383138363300000073.097.0NaNNaNNaNNaNNaN-26146.0NaN-57.0NaNPARISVehicle Connected324333373044iOS438.00.3.3.01.7.18.01.0
99936f700383-48f2-4aa7-9ede-d0437f90c09ff4b7ecf1-5feb-4197-8bca-883513fc63af8949.0DA210815.102023-05-16 13:49:362023.3.172.0NaN0.0HT1002MA12152049585.098.0NaNNaNNaNNaNNaN-24992.0NaN-65.0NaNPARISVehicle Connected5F1807iOS510.00.3.3.01.7.28.01.0
9994bd1e99fc-086e-4260-9cc4-f291d63002d9b59b0854-5153-4e62-96df-db84293162cf2713.0DA210815.102023-07-07 07:22:272023.6.122.0NaN0.0NaN46.098.0NaNNaNNaNNaNNaN-30683.0NaN-11007.0NaNPARISVehicle Connected22510450iOS276.01.0.6.065.1.39.342.0
999589f28faa-77ef-4022-a767-86c8eeb42d2ff958f248-e4cc-40c3-8e6b-d0e4b8429c4621101.0DA210815.102023-09-11 14:40:102023.9.42.0NaN0.0NaN73.096.0NaNNaNNaNNaNNaN-26271.0NaN-63.0NaNHAMBURGVehicle Connected2CBE66iOS438.01.0.6.01.2.28.00.0
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Duplicate rows

Most frequently occurring

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